Overview

Dataset statistics

Number of variables15
Number of observations4872
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory571.1 KiB
Average record size in memory120.0 B

Variable types

NUM13
CAT2

Warnings

citric_acid has 117 (2.4%) zeros Zeros

Reproduction

Analysis started2020-11-21 16:50:49.402039
Analysis finished2020-11-21 16:52:25.962095
Duration1 minute and 36.56 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

df_index
Real number (ℝ≥0)

Distinct3974
Distinct (%)81.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2041.144089
Minimum0
Maximum4897
Zeros2
Zeros (%)< 0.1%
Memory size38.1 KiB
2020-11-21T22:22:26.168069image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile152.1
Q1807
median1652.5
Q33283.25
95-th percentile4575.45
Maximum4897
Range4897
Interquartile range (IQR)2476.25

Descriptive statistics

Standard deviation1441.655691
Coefficient of variation (CV)0.7062978546
Kurtosis-1.117858499
Mean2041.144089
Median Absolute Deviation (MAD)1101.5
Skewness0.4119395107
Sum9944454
Variance2078371.131
MonotocityNot monotonic
2020-11-21T22:22:26.442712image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
62< 0.1%
 
2622< 0.1%
 
2862< 0.1%
 
2942< 0.1%
 
3022< 0.1%
 
3062< 0.1%
 
3102< 0.1%
 
3182< 0.1%
 
3302< 0.1%
 
3382< 0.1%
 
Other values (3964)485299.6%
 
ValueCountFrequency (%) 
02< 0.1%
 
12< 0.1%
 
21< 0.1%
 
32< 0.1%
 
41< 0.1%
 
ValueCountFrequency (%) 
48971< 0.1%
 
48961< 0.1%
 
48951< 0.1%
 
48941< 0.1%
 
48931< 0.1%
 

fixed_acidity
Real number (ℝ≥0)

Distinct105
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.234308292
Minimum3.8
Maximum15.9
Zeros0
Zeros (%)0.0%
Memory size38.1 KiB
2020-11-21T22:22:26.725121image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum3.8
5-th percentile5.7
Q16.4
median7
Q37.7
95-th percentile9.9
Maximum15.9
Range12.1
Interquartile range (IQR)1.3

Descriptive statistics

Standard deviation1.324478017
Coefficient of variation (CV)0.1830828828
Kurtosis5.01084937
Mean7.234308292
Median Absolute Deviation (MAD)0.6
Skewness1.744912248
Sum35245.55
Variance1.754242018
MonotocityNot monotonic
2020-11-21T22:22:26.999324image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
6.82685.5%
 
6.62414.9%
 
6.42274.7%
 
6.92164.4%
 
7.22054.2%
 
6.71934.0%
 
7.11923.9%
 
71883.9%
 
6.51793.7%
 
7.31763.6%
 
Other values (95)278757.2%
 
ValueCountFrequency (%) 
3.81< 0.1%
 
4.22< 0.1%
 
4.42< 0.1%
 
4.51< 0.1%
 
4.62< 0.1%
 
ValueCountFrequency (%) 
15.91< 0.1%
 
15.61< 0.1%
 
15.52< 0.1%
 
151< 0.1%
 
14.31< 0.1%
 

volatile_acidity
Real number (ℝ≥0)

Distinct185
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3435724548
Minimum0.08
Maximum1.58
Zeros0
Zeros (%)0.0%
Memory size38.1 KiB
2020-11-21T22:22:27.280724image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.08
5-th percentile0.16
Q10.23
median0.3
Q30.41
95-th percentile0.68
Maximum1.58
Range1.5
Interquartile range (IQR)0.18

Descriptive statistics

Standard deviation0.1669408145
Coefficient of variation (CV)0.4858969693
Kurtosis3.024510996
Mean0.3435724548
Median Absolute Deviation (MAD)0.08
Skewness1.531190873
Sum1673.885
Variance0.02786923556
MonotocityNot monotonic
2020-11-21T22:22:27.570590image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.242034.2%
 
0.281984.1%
 
0.261984.1%
 
0.251903.9%
 
0.221793.7%
 
0.231673.4%
 
0.271623.3%
 
0.31623.3%
 
0.21603.3%
 
0.321533.1%
 
Other values (175)310063.6%
 
ValueCountFrequency (%) 
0.081< 0.1%
 
0.0851< 0.1%
 
0.091< 0.1%
 
0.140.1%
 
0.10530.1%
 
ValueCountFrequency (%) 
1.581< 0.1%
 
1.332< 0.1%
 
1.241< 0.1%
 
1.181< 0.1%
 
1.131< 0.1%
 

citric_acid
Real number (ℝ≥0)

ZEROS

Distinct89
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3189634647
Minimum0
Maximum1.66
Zeros117
Zeros (%)2.4%
Memory size38.1 KiB
2020-11-21T22:22:27.998657image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.05
Q10.25
median0.31
Q30.39
95-th percentile0.5645
Maximum1.66
Range1.66
Interquartile range (IQR)0.14

Descriptive statistics

Standard deviation0.1459710514
Coefficient of variation (CV)0.4576419167
Kurtosis2.720463079
Mean0.3189634647
Median Absolute Deviation (MAD)0.07
Skewness0.5174778139
Sum1553.99
Variance0.02130754783
MonotocityNot monotonic
2020-11-21T22:22:28.292310image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.32575.3%
 
0.282314.7%
 
0.262024.1%
 
0.322024.1%
 
0.491984.1%
 
0.341883.9%
 
0.291843.8%
 
0.311733.6%
 
0.271683.4%
 
0.241673.4%
 
Other values (79)290259.6%
 
ValueCountFrequency (%) 
01172.4%
 
0.01260.5%
 
0.02390.8%
 
0.03240.5%
 
0.04310.6%
 
ValueCountFrequency (%) 
1.661< 0.1%
 
1.231< 0.1%
 
140.1%
 
0.991< 0.1%
 
0.912< 0.1%
 

residual_sugar
Real number (ℝ≥0)

Distinct299
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.442692939
Minimum0.6
Maximum65.8
Zeros0
Zeros (%)0.0%
Memory size38.1 KiB
2020-11-21T22:22:28.586767image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.6
5-th percentile1.2
Q11.8
median3.05
Q38.1
95-th percentile15
Maximum65.8
Range65.2
Interquartile range (IQR)6.3

Descriptive statistics

Standard deviation4.747205898
Coefficient of variation (CV)0.8722163736
Kurtosis5.758810471
Mean5.442692939
Median Absolute Deviation (MAD)1.75
Skewness1.534818269
Sum26516.8
Variance22.53596384
MonotocityNot monotonic
2020-11-21T22:22:28.857709image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
21723.5%
 
1.41643.4%
 
1.81583.2%
 
1.61583.2%
 
1.21513.1%
 
2.11412.9%
 
2.21382.8%
 
1.91342.8%
 
1.51312.7%
 
2.31272.6%
 
Other values (289)339869.7%
 
ValueCountFrequency (%) 
0.62< 0.1%
 
0.760.1%
 
0.8170.3%
 
0.9280.6%
 
0.952< 0.1%
 
ValueCountFrequency (%) 
65.81< 0.1%
 
31.62< 0.1%
 
26.051< 0.1%
 
23.51< 0.1%
 
22.61< 0.1%
 

chlorides
Real number (ℝ≥0)

Distinct197
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0561430624
Minimum0.009
Maximum0.611
Zeros0
Zeros (%)0.0%
Memory size38.1 KiB
2020-11-21T22:22:29.140619image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.009
5-th percentile0.028
Q10.038
median0.047
Q30.066
95-th percentile0.1
Maximum0.611
Range0.602
Interquartile range (IQR)0.028

Descriptive statistics

Standard deviation0.03523188657
Coefficient of variation (CV)0.6275376702
Kurtosis53.05130162
Mean0.0561430624
Median Absolute Deviation (MAD)0.011
Skewness5.487260087
Sum273.529
Variance0.001241285832
MonotocityNot monotonic
2020-11-21T22:22:29.419654image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.0361623.3%
 
0.0441473.0%
 
0.0421422.9%
 
0.0461412.9%
 
0.0471362.8%
 
0.051342.8%
 
0.0481342.8%
 
0.041302.7%
 
0.0341262.6%
 
0.0451262.6%
 
Other values (187)349471.7%
 
ValueCountFrequency (%) 
0.0091< 0.1%
 
0.01230.1%
 
0.0131< 0.1%
 
0.01440.1%
 
0.01530.1%
 
ValueCountFrequency (%) 
0.6111< 0.1%
 
0.611< 0.1%
 
0.4641< 0.1%
 
0.4221< 0.1%
 
0.4151< 0.1%
 

free_sulfur_dioxide
Real number (ℝ≥0)

Distinct132
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.61514778
Minimum1
Maximum289
Zeros0
Zeros (%)0.0%
Memory size38.1 KiB
2020-11-21T22:22:29.740507image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q117
median29
Q341
95-th percentile61
Maximum289
Range288
Interquartile range (IQR)24

Descriptive statistics

Standard deviation17.96642208
Coefficient of variation (CV)0.5868474719
Kurtosis9.92286932
Mean30.61514778
Median Absolute Deviation (MAD)12
Skewness1.38036536
Sum149157
Variance322.7923223
MonotocityNot monotonic
2020-11-21T22:22:30.158039image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
291372.8%
 
61302.7%
 
261202.5%
 
151172.4%
 
311162.4%
 
171152.4%
 
341152.4%
 
241142.3%
 
351132.3%
 
211082.2%
 
Other values (122)368775.7%
 
ValueCountFrequency (%) 
11< 0.1%
 
21< 0.1%
 
3481.0%
 
4410.8%
 
51032.1%
 
ValueCountFrequency (%) 
2891< 0.1%
 
146.51< 0.1%
 
138.51< 0.1%
 
1311< 0.1%
 
1281< 0.1%
 

total_sulfur_dioxide
Real number (ℝ≥0)

Distinct272
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean116.0587028
Minimum6
Maximum440
Zeros0
Zeros (%)0.0%
Memory size38.1 KiB
2020-11-21T22:22:30.435860image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile19
Q178
median118
Q3156
95-th percentile206
Maximum440
Range434
Interquartile range (IQR)78

Descriptive statistics

Standard deviation56.9460764
Coefficient of variation (CV)0.4906661459
Kurtosis-0.3094440636
Mean116.0587028
Median Absolute Deviation (MAD)39
Skewness0.01981624191
Sum565438
Variance3242.855617
MonotocityNot monotonic
2020-11-21T22:22:30.722844image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
111541.1%
 
113501.0%
 
118440.9%
 
110440.9%
 
128430.9%
 
98430.9%
 
132420.9%
 
122410.8%
 
133400.8%
 
131400.8%
 
Other values (262)443190.9%
 
ValueCountFrequency (%) 
62< 0.1%
 
72< 0.1%
 
8100.2%
 
9140.3%
 
10240.5%
 
ValueCountFrequency (%) 
4401< 0.1%
 
366.51< 0.1%
 
3441< 0.1%
 
3131< 0.1%
 
307.51< 0.1%
 

density
Real number (ℝ≥0)

Distinct935
Distinct (%)19.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9947374312
Minimum0.98711
Maximum1.03898
Zeros0
Zeros (%)0.0%
Memory size38.1 KiB
2020-11-21T22:22:30.972848image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.98711
5-th percentile0.9899255
Q10.9924
median0.99497
Q30.997
95-th percentile0.9994
Maximum1.03898
Range0.05187
Interquartile range (IQR)0.0046

Descriptive statistics

Standard deviation0.003031426585
Coefficient of variation (CV)0.003047464074
Kurtosis8.614202103
Mean0.9947374312
Median Absolute Deviation (MAD)0.00227
Skewness0.6344798903
Sum4846.360765
Variance9.189547142e-06
MonotocityNot monotonic
2020-11-21T22:22:31.244795image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.9976541.1%
 
0.9962481.0%
 
0.998481.0%
 
0.992471.0%
 
0.9958471.0%
 
0.9972471.0%
 
0.9966460.9%
 
0.9986460.9%
 
0.9968460.9%
 
0.9928450.9%
 
Other values (925)439890.3%
 
ValueCountFrequency (%) 
0.987111< 0.1%
 
0.987131< 0.1%
 
0.98741< 0.1%
 
0.987422< 0.1%
 
0.987461< 0.1%
 
ValueCountFrequency (%) 
1.038981< 0.1%
 
1.01032< 0.1%
 
1.003692< 0.1%
 
1.00321< 0.1%
 
1.003152< 0.1%
 

pH
Real number (ℝ≥0)

Distinct106
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.217754516
Minimum2.72
Maximum4.01
Zeros0
Zeros (%)0.0%
Memory size38.1 KiB
2020-11-21T22:22:31.562578image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2.72
5-th percentile2.97
Q13.11
median3.2
Q33.32
95-th percentile3.5
Maximum4.01
Range1.29
Interquartile range (IQR)0.21

Descriptive statistics

Standard deviation0.1604890355
Coefficient of variation (CV)0.04987609675
Kurtosis0.3759502829
Mean3.217754516
Median Absolute Deviation (MAD)0.1
Skewness0.3838651215
Sum15676.9
Variance0.02575673053
MonotocityNot monotonic
2020-11-21T22:22:31.969211image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
3.161573.2%
 
3.141443.0%
 
3.181362.8%
 
3.191322.7%
 
3.151312.7%
 
3.21312.7%
 
3.221302.7%
 
3.11202.5%
 
3.261172.4%
 
3.171162.4%
 
Other values (96)355873.0%
 
ValueCountFrequency (%) 
2.721< 0.1%
 
2.742< 0.1%
 
2.771< 0.1%
 
2.7930.1%
 
2.830.1%
 
ValueCountFrequency (%) 
4.011< 0.1%
 
3.92< 0.1%
 
3.851< 0.1%
 
3.811< 0.1%
 
3.81< 0.1%
 

sulphates
Real number (ℝ≥0)

Distinct107
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5324753695
Minimum0.22
Maximum2
Zeros0
Zeros (%)0.0%
Memory size38.1 KiB
2020-11-21T22:22:32.278783image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.22
5-th percentile0.35
Q10.43
median0.51
Q30.6
95-th percentile0.79
Maximum2
Range1.78
Interquartile range (IQR)0.17

Descriptive statistics

Standard deviation0.1484505251
Coefficient of variation (CV)0.2787932244
Kurtosis8.668378876
Mean0.5324753695
Median Absolute Deviation (MAD)0.08
Skewness1.790216574
Sum2594.22
Variance0.02203755842
MonotocityNot monotonic
2020-11-21T22:22:32.551111image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.51994.1%
 
0.541813.7%
 
0.461803.7%
 
0.441693.5%
 
0.521583.2%
 
0.381563.2%
 
0.481523.1%
 
0.471493.1%
 
0.491483.0%
 
0.531463.0%
 
Other values (97)323466.4%
 
ValueCountFrequency (%) 
0.221< 0.1%
 
0.231< 0.1%
 
0.2530.1%
 
0.2640.1%
 
0.2790.2%
 
ValueCountFrequency (%) 
21< 0.1%
 
1.952< 0.1%
 
1.611< 0.1%
 
1.591< 0.1%
 
1.561< 0.1%
 

alcohol
Real number (ℝ≥0)

Distinct103
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.48427477
Minimum8
Maximum14.9
Zeros0
Zeros (%)0.0%
Memory size38.1 KiB
2020-11-21T22:22:32.816966image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile8.9
Q19.5
median10.3
Q311.3
95-th percentile12.7
Maximum14.9
Range6.9
Interquartile range (IQR)1.8

Descriptive statistics

Standard deviation1.196296361
Coefficient of variation (CV)0.1141038734
Kurtosis-0.5171509477
Mean10.48427477
Median Absolute Deviation (MAD)0.9
Skewness0.5770674868
Sum51079.38667
Variance1.431124983
MonotocityNot monotonic
2020-11-21T22:22:33.077254image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
9.52795.7%
 
9.42605.3%
 
9.21974.0%
 
101783.7%
 
10.51753.6%
 
9.81623.3%
 
91573.2%
 
111573.2%
 
10.41453.0%
 
9.61443.0%
 
Other values (93)301861.9%
 
ValueCountFrequency (%) 
82< 0.1%
 
8.450.1%
 
8.580.2%
 
8.6210.4%
 
8.7591.2%
 
ValueCountFrequency (%) 
14.91< 0.1%
 
14.21< 0.1%
 
14.051< 0.1%
 
1490.2%
 
13.92< 0.1%
 

quality
Real number (ℝ≥0)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.806034483
Minimum3
Maximum9
Zeros0
Zeros (%)0.0%
Memory size38.1 KiB
2020-11-21T22:22:33.292501image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile5
Q15
median6
Q36
95-th percentile7
Maximum9
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8736314442
Coefficient of variation (CV)0.1504695583
Kurtosis0.2020304925
Mean5.806034483
Median Absolute Deviation (MAD)1
Skewness0.1718342546
Sum28287
Variance0.7632319003
MonotocityNot monotonic
2020-11-21T22:22:33.467503image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%) 
6209943.1%
 
5163033.5%
 
781216.7%
 
41683.4%
 
81362.8%
 
3240.5%
 
930.1%
 
ValueCountFrequency (%) 
3240.5%
 
41683.4%
 
5163033.5%
 
6209943.1%
 
781216.7%
 
ValueCountFrequency (%) 
930.1%
 
81362.8%
 
781216.7%
 
6209943.1%
 
5163033.5%
 

quality_level
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size38.1 KiB
Good
3729 
Excellent
951 
Bad
 
192
ValueCountFrequency (%) 
Good372976.5%
 
Excellent95119.5%
 
Bad1923.9%
 
2020-11-21T22:22:33.701859image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-21T22:22:33.948924image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:34.129667image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length9
Median length4
Mean length4.936576355
Min length3

wine_type
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.1 KiB
white
3656 
red
1216 
ValueCountFrequency (%) 
white365675.0%
 
red121625.0%
 
2020-11-21T22:22:34.348415image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-21T22:22:34.474947image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:34.632430image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length5
Median length5
Mean length4.500821018
Min length3

Interactions

2020-11-21T22:21:36.703074image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:21:37.062390image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:21:37.296767image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:21:37.541100image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:21:37.813092image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:21:38.150257image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:21:38.486828image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:21:38.764494image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:21:39.037285image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:21:39.290028image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:21:39.575468image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:21:39.948236image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:21:40.245717image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:21:40.810009image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:21:41.066599image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:21:41.363939image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:21:41.668342image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:21:41.904433image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:21:42.138810image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:21:42.373953image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:21:42.623952image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:21:42.885545image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:21:43.143739image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:21:43.413292image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:21:43.663292image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:21:43.910073image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:21:44.128815image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:21:44.411797image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:21:44.665498image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:21:44.985458image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:21:45.293303image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:21:45.588732image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:21:45.866913image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:21:46.141108image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:21:46.407740image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:21:46.735186image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:21:47.000168image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:21:47.265794image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:21:47.591420image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:21:47.841411image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:21:48.075787image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:21:48.444125image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:21:48.871857image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:21:49.204843image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:21:49.506405image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:21:49.790445image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:21:50.215136image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:21:50.634136image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:21:51.172868image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:21:51.516563image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:21:51.768448image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:21:52.018461image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:21:52.302706image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:21:52.539375image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:21:52.778557image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:21:53.096045image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:21:53.404047image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:21:53.707499image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:21:53.979620image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:21:54.342273image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:21:54.709254image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:21:54.959258image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:21:55.289303image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:21:55.672603image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:21:55.942230image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:21:56.251331image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:21:56.694113image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:21:56.940088image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:21:57.632087image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:21:57.908110image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:21:58.157112image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:21:58.492340image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:21:58.778174image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:21:59.053780image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:21:59.319502image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:21:59.569474image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:21:59.819454image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:00.100932image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:00.523085image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:00.771975image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:01.123962image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:01.388671image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:01.636564image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:01.888389image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:02.196813image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:02.486771image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:02.716237image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:02.950628image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:03.207017image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:03.443018image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:03.709355image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:03.947488image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:04.176333image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:04.417056image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:04.702867image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:05.041870image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:05.328570image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:05.547328image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:05.765913image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:05.969016image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:06.222964image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:06.523058image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:06.773849image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:07.043003image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:07.300671image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:07.548312image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:07.804318image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:08.054960image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:08.285980image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:08.528839image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:08.801426image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:09.042656image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:09.245760image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:09.496585image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:09.715357image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:09.978337image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:10.250424image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:10.529945image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:10.778578image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:11.024564image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:11.258954image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:11.496099image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:11.744012image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:11.995373image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:12.310331image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:12.535610image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:12.754338image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:12.992516image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:13.226882image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:13.529512image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:13.748259image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:13.995021image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:14.229383image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:14.496809image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:14.746789image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:14.998539image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:15.232922image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:15.562837image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:15.875331image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:16.172942image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:16.439131image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:16.736965image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:17.010510image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:17.294619image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:17.541489image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:17.786487image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:18.059488image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:18.405467image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:18.787238image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:19.033432image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:19.281279image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:19.512631image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:19.856187image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:20.568849image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:20.835863image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:21.085836image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:21.338709image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:21.571819image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:21.830004image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:22.093209image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:22.322716image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:22.579822image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:22.824125image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:23.088692image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:23.321977image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:23.566517image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:23.844911image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:24.108281image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:24.371663image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2020-11-21T22:22:34.983652image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-11-21T22:22:35.560475image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-11-21T22:22:35.995895image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-11-21T22:22:36.461818image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-11-21T22:22:36.921986image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-11-21T22:22:24.945309image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T22:22:25.624119image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

df_indexfixed_acidityvolatile_aciditycitric_acidresidual_sugarchloridesfree_sulfur_dioxidetotal_sulfur_dioxidedensitypHsulphatesalcoholqualityquality_levelwine_type
033725.60.150.265.550.05151.0139.00.993363.470.5011.0000006Goodwhite
110947.40.240.318.400.04552.0183.00.996303.090.328.8000005Goodwhite
215146.90.840.214.100.07416.065.00.998423.530.729.2333336Goodred
38537.70.340.5811.100.03941.0151.00.997803.060.498.6000005Goodwhite
437846.50.250.2717.400.06429.0140.00.997763.200.4910.1000006Goodwhite
536087.00.160.2514.300.04427.0149.00.998002.910.469.2000006Goodwhite
6227.90.430.211.600.10610.037.00.996603.170.919.5000005Goodred
71187.20.310.5013.300.05668.0195.00.998203.010.479.2000005Goodwhite
810019.90.350.381.500.05831.047.00.996763.260.8210.6000007Excellentred
934616.70.240.303.850.042105.0179.00.991893.040.5911.3000008Excellentwhite

Last rows

df_indexfixed_acidityvolatile_aciditycitric_acidresidual_sugarchloridesfree_sulfur_dioxidetotal_sulfur_dioxidedensitypHsulphatesalcoholqualityquality_levelwine_type
48629598.00.5900.052.00.08912.032.00.997353.360.6110.05Goodred
486315938.60.1600.497.30.0439.063.00.995303.130.5910.56Goodwhite
48643457.00.6850.001.90.06740.063.00.997903.600.819.95Goodred
486513527.60.6450.031.90.08614.057.00.996903.370.4610.35Goodred
48669648.50.4700.271.90.05818.038.00.995183.160.8511.16Goodred
48673209.80.6600.393.20.08321.059.00.998903.370.7111.57Excellentred
486840606.40.4100.016.10.04820.070.00.993623.190.4210.05Goodwhite
486913467.00.4600.396.20.03946.0163.00.992803.210.3512.27Excellentwhite
487034545.80.5400.001.40.03340.0107.00.989183.260.3512.45Goodwhite
487135826.30.3200.321.50.03712.076.00.989933.300.4612.36Goodwhite